Dendrites receive the majority of synaptic inputs and therefore play a central role in integrating incoming information. The transfer of excitatory post-synaptic potentials along the dendritic tree represents an excellent opportunity to transform this information before it reaches the axon where action potentials are initiated. Due to the difficulty of recording from the dendrites, our understanding of these signal transformations remains rudimentary. Thus, we rely on computational models of dendrites for predictions and insights into their biophysical mechanisms and functional ramifications. In this project, we will develop a novel electrophysiological technique, Dendritic Replacement Dynamic Clamp (DRDclp), that combines somatic dynamic clamp with computer modeling to replace biological dendrites with virtual ones. To perform DRDclp: 1). native dendrites will first be uncoupled from the soma to minimize their electrical impact on somatic current injection; 2). virtual dendrites will then be attached by injecting the axial current between a real-time simulated cable model and the soma via dynamic clamp. As the native spike generating mechanisms will remain functionally intact, synaptic integration will be studied by observing the effects of varying dendritic properties on neuronal output. Two broad classes of virtual dendrite will used: Duplicate virtual dendrites will be parameter fitted so that their properties match those of the native dendrites they replace; alternative virtual dendrites will be based on dendrite models for the neuronal class in question however their properties will be judiciously chosen to test some hypotheses. In Aim 1, we will use physical uncoupling with alternative virtual dendrites to probe an example problem of synaptic integration in two model systems: gerbil medial superior olive (MSO) neurons and rat cortical pyramidal neurons. By using two neuronal classes that possess different dendritic and electrical properties, we plan to demonstrate the universality of the DRDclp technique. In Aim 2, we not only propose to use DRDclp as a means of attaching duplicate virtual dendrites to gerbil MSO neurons, but also to perform electrical uncoupling by cancelling the current entering or leaving native dendrites. Real-time parameter fitting of our dendrite model will be necessary to implement electrical cancellation and virtual dendritic attachment successfully in this aim. Experimental data generated by targeting synaptic stimulation and channel block along individual dendrites will be used to facilitate this calibration. Simultaneous recordings from dendrites and soma will also help to confirm the success of electrical cancellation. We expect DRDclp can be used to enrich our understanding of dendritic computations while also making sense of the variability in dendritic properties in neuronal populations.